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Semi-supervised local feature selection for data classification

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Science China(Information Sciences) 2021年第9期64卷 127-138页

作者： Zechao LI Jinhui TANG School of Computer Science and Engineering Nanjing University of Science and Technology

Conventional feature selection methods select the same feature subset for all classes, which means that the selected features might work better for some classes than the others. Towards this end, this paper proposes a new semi-supervised local feature selection method(S2 LFS) allowing to select different feature subsets for different classes. According to this method, class-specific feature subsets are selected by learning the importance of features considering each class separately. In particular, the class labels of all available data are jointly learned under a consistent constraint over the labeled data, which enables the proposed method to select the most discriminative features. Experiments on six data sets demonstrate the effectiveness of the proposed method compared to some popular feature selection methods.

关键词： local feature selection label-specific feature semi-supervised learning data classification discriminative feature

data classification method based on network dynamics analysis and cloud model

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Journal of Measurement Science and Instrumentation 2016年第3期7卷 266-271页

作者：王肖霞杨风暴梁若飞张文华中北大学信息与通信工程学院山西太原030051

In order to reduce amount of data storage and improve processing capacity of the system, this paper proposes a new classification method of data source by combining phase synchronization model in network clustering with cloud model. Firstly, taking data source as a complex network, after the topography of network is obtained, the cloud model of each node data is determined by fuzzy analytic hierarchy process （AHP）. Secondly, by calculating expectation, entropy and hyper entropy of the cloud model, comprehensive coupling strength is got and then it is regarded as the edge weight of topography. Finally, distribution curve is obtained by iterating the phase of each node by means of phase synchronization model. Thus classification of data source is completed. This method can not only provide convenience for storage, cleaning and compression of data, but also improve the efficiency of data analysis.

关键词： data classification complex network phase synchronization cloud model

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Metaheuristic Based Clustering with Deep Learning Model for Big data classification

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Computer Systems Science & Engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

A novel compound data classification method and its application in fault diagnosis of rolling bearings

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International Journal of Intelligent Computing and Cybernetics 2017年第1期10卷 80-90页

作者： Anxin Sun Ying Che College of Optical and Electronic Information Changchun University of Science and TechnologyChangchunChina

Purpose-The purpose of this paper is to provide a fault diagnosis method for rolling *** bearings are widely used in industrial appliances,and their fault diagnosis is of great importance and has drawn more and more *** on the common failure mechanism of failure modes of rolling bearings,this paper proposes a novel compound data classification method based on the discrete wavelet transform and the support vector machine(SVM)and applies it in the fault diagnosis of rolling ***/methodology/approach-Vibration signal contains large quantity of information of bearing status and this paper uses various types of wavelet base functions to perform discrete wavelet transform of vibration and *** vectors are constructed based on several time-domain indices of the denoised *** is then used to perform classification and fault *** the optimal wavelet base function is determined based on the diagnosis ***-Experiments of fault diagnosis of rolling bearings are carried out and wavelet functions in several wavelet families were *** results show that the SVM classifier with the db4 wavelet base function in the db wavelet family has the best fault diagnosis ***/value-This method provides a practical candidate for the fault diagnosis of rolling bearings in the industrial applications.

关键词： Support vector machine Fault diagnosis data classification Discrete wavelet transform Rolling bearing

A Method for data classification Based on Discernibility Matrix and Discernibility Function

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 230-233页

作者： SUN Shi-bao QIN Ke-yun School of Science Southwest Jiaotong UniversityChengdu 610031 Sichuan China Electronic Information Engineering College Henan University of Science and Technology Luoyang 471003Henan China

A method for data classification will influence the efficiency of classification. Attributes reduction based on discernibility matrix and discernibility function in rough sets can use in data classification, so we put forward a method for data classification. Namely, firstly, we use discernibility matrix and discernibility function to delete superfluous attributes in formation system and get a necessary attribute set. Secondly, we delete superfluous attribute values and get decision rules. Finally, we classify data by means of decision rules. The experiments show that data classification using this method is simpler in the structure, and can improve the efficiency of classification.

关键词： discernibility matrix discernibility function attributes reduction data classification

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical data classification

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Computer Systems Science & Engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and classification for Healthcare data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

Performance Comparison of data classification based on Modern Convolutional Neural Network Architectures

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Performance Comparison of Data Classification based on Moder...

An Improved Particle Swarm Optimization with EA Mutation for data classification

第三十九届中国控制会议

作者： Yuchen Tan Yanxiang Li Han Liu Wang Lu Xiaowu Xiao Institute for Cultural Heritage and History of Science and Technology University of Science and Technology Beijing The Shenyang Palace Museum Henan Museum School of Automation Beijing Institute of Technology

In recent years, convolutional neural network（CNN） has become the most powerful tool to deal with some computer vision applications, for example, classification, object detection, semantic segmentation, and so on. This benefits from the development of computer hardware and theory of deep learning. Many state-of-the-art CNN architectures have been proposed and they produce better performance than previous conventional methods. However, the effects of these modern CNN architectures are usually demonstrated based on some public datasets. The performance of these architectures on unpublished datasets is still not clear. To solve this problem, we compare classification performance of six different CNN architectures based on our own Lead isotope dataset. Six architectures are ZFNet, VGG16, ResNet18, ResNet101, GoogLeNet, DenseNet, *** experiments, we find that all CNN architectures can produce satisfactory classification results, and the performance of some architectures in our dataset is different from that in some public datasets.

关键词： data classification Convolutional Neural Networks Deep Learning Lead Isotope

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An Improved Particle Swarm Optimization with EA Mutation for...

Research on data classification method based on class attribute mathematical expectation

2009 ISECS International Colloquium on Computing, Communication, Control, and Management

作者： LIU Qiu-lian Department of Computer Science and Technology GuangDong University of Finance Guangzhou 510521,China

data classification is an important data mining *** optimization techniques have been proposed to improve the performance of data *** this paper we propose a novel algorithm for data classification that we call particle swarm optimization with EA *** evaluate its usefulness,we empirically compare the performance of our algorithm with another evolutionary algorithm,namely a Genetic Algorithm,in rule discovery for classification *** tasks are considered core tools for Decision Support Systems in a widespread urea,ranging from the industry,commerce,military and scientific *** data sources used here for experimental testing are commonly used and considered us a standard for rule discovery algorithms reliability *** results obtained in these domains seem to indicate that our algorithm is competitive with other evolutionary techniques.

关键词： Particle Swarm Optimization Mutation data classification

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Research on data classification method based on class attrib...